Applied Predictive Modeling

Abstract

This is a book on data analysis with a specific focus on the practice of predictive modeling. The term predictive modeling may stir associations such as machine learning, pattern recognition, and data mining. Indeed, these associations are appropriate and the methods implied by these terms are an integral piece of the predictive modeling process. But predictive modeling encompasses much more than the tools and techniques for uncovering patterns within data. The practice of predictive modeling defines the process of developing a model in a way that we can understand and quantify the model’s prediction accuracy on future, yet-to-be-seen data. The entire process is the focus of this book. We intend this work to be a practitioner’s guide to the predictive modeling process and a place where one can come to learn about the approach and to gain intuition about the many commonly used and modern, powerful models. A host of statistical and mathematical techniques are discussed, but our motivation in almost every case is to describe the techniques in a way that helps develop intuition for its strengths and weaknesses instead of its mathematical genesis and underpinnings. For the most part we avoid complex equations, although there are a few necessary exceptions. For more theoretical treatments of predictive modeling, we suggest Hastie et al. (2008) and Bishop (2006). For this text, the reader should have some knowledge of basic statistics, including variance, correlation, simple linear regression, and basic hypothesis testing (e.g. p-values and test statistics). The predictive modeling process is inherently hands-on. But during our research for this work we found that many articles and texts prevent the reader from reproducing the results either because the data were not freely available or because the software was inaccessible or only available for purchase. Buckheit and Donoho (1995) provide a relevant critique of the traditional scholarly veil: